Middle East respiratory syndrome coronavirus (MERS-CoV) causes high fever, cough, acute respiratory tract infection and multiorgan dysfunction that may eventually lead to the death of the infected individuals. MERS-CoV is thought to be transmitted to humans through dromedary camels. The occurrence of the virus was first reported in the Middle East and it subsequently spread to several parts of the world. Since 2012, about 1368 infections, including ~487 deaths, have been reported worldwide. Notably, the recent human-to-human ‘superspreading' of MERS-CoV in hospitals in South Korea has raised a major global health concern. The fatality rate in MERS-CoV infection is four times higher compared with that of the closely related severe acute respiratory syndrome coronavirus infection. Currently, no drug has been clinically approved to control MERS-CoV infection. In this study, we highlight the potential drug targets that can be used to develop anti-MERS-CoV therapeutics.

Lipopolysaccharide (LPS) is a component of the outer membrane of mainly Gram-negative bacteria and cyanobacteria. The LPS molecules from marine and terrestrial bacteria show structural variations, even among strains within the same species living in the same environment. Cyanobacterial LPS has a unique structure, since it lacks heptose and 3-deoxy-d-manno-octulosonic acid (also known as keto-deoxyoctulosonate (KDO)), which are present in the core region of common Gram-negative LPS. In addition, the cyanobacterial lipid A region lacks phosphates and contains odd-chain hydroxylated fatty acids. While the role of Gram-negative lipid A in the regulation of the innate immune response through Toll-like Receptor (TLR) 4 signaling is well characterized, the role of the structurally different cyanobacterial lipid A in TLR4 signaling is not well understood. The uncontrolled inflammatory response of TLR4 leads to autoimmune diseases such as sepsis, and thus the less virulent marine cyanobacterial LPS molecules can be effective to inhibit TLR4 signaling. This review highlights the structural comparison of LPS molecules from marine cyanobacteria and Gram-negative bacteria. We discuss the potential use of marine cyanobacterial LPS as a TLR4 antagonist, and the effects of cyanobacterial LPS on humans and marine organisms.

Toll-like receptor 2 (TLR2) responses are involved in various inflammatory immune disorders. Phloretin is a naturally occurring dietary flavonoid that is abundant in fruit. Here, we investigated whether the anti-inflammatory activity of phloretin is mediated through TLR2 pathways, and whether phloretin acts as an inhibitor of TLR2/1 heterodimerization using the TLR2/1 agonist Pam3CSK4. We tested the effects of phloretin on tumor necrosis factor (TNF)-α production induced by various TLRs using known TLR-specific agonists. Phloretin significantly inhibited Pam3CSK4-induced TRL2/1 signaling in Raw264.7 cells compared to TLR signaling induced by the other agonists tested. Therefore, we further tested the effects of phloretin in human embryonic kidney (HEK) 293-hTLR2 cells induced by Pam3CSK4, and confirmed that phloretin has comparable inhibition of TLR2/1 heterodimerization to that induced by the known TLR2 inhibitor CU-CPT22. Moreover, phloretin reduced the secretion of the inflammatory cytokines TNF-α and interleukin (IL)-8 in Pam3CSK4-induced HEK293-hTLR2 cells, whereas it did not significantly reduce these cytokines under Pam2CSK4-induced activation. Western blot results showed that phloretin significantly suppressed Pam3CSK4-induced TLR2 and NF-κB p65 expression. The molecular interactions between phloretin and TLR2 were investigated using bio-layer interferometry and in silico docking. Phloretin bound to TLR2 with micromolar binding affinity, and we proposed a binding model of phloretin at the TLR2–TLR1 interface. Overall, we confirmed that phloretin inhibits the heterodimerization of TLR2/1, highlighting TLR2 signaling as a therapeutic target for treating TLR2-mediated inflammatory immune diseases.

The interleukin-1 receptor-associated kinase (IRAK) family comprises critical signaling mediators of the TLR/IL-1R signaling pathways. IRAKs are Ser/Thr kinases. There are 4 members in the vertebrate genome (IRAK1, IRAK2, IRAKM, and IRAK4) and an IRAK homolog, Pelle, in insects. IRAK family members are highly conserved in vertebrates, but the evolutionary relationship between IRAKs in vertebrates and insects is not clear. To investigate the evolutionary history and functional divergence of IRAK members, we performed extensive bioinformatics analysis. The phylogenetic relationship between IRAK sequences suggests that gene duplication events occurred in the evolutionary lineage, leading to early vertebrates. A comparative phylogenetic analysis with insect homologs of IRAKs suggests that the Tube protein is a homolog of IRAK4, unlike the anticipated protein, Pelle. Furthermore, the analysis supports that an IRAK4-like kinase is an ancestral protein in the metazoan lineage of the IRAK family. Through functional analysis, several potentially diverged sites were identified in the common death domain and kinase domain. These sites have been constrained during evolution by strong purifying selection, suggesting their functional importance within IRAKs. In summary, our study highlighted the molecular evolution of the IRAK family, predicted the amino acids that contributed to functional divergence, and identified structural variations among the IRAK paralogs that may provide a starting point for further experimental investigations.

Chemical similarity searching is a basic research tool that can be used to find small molecules which are similar in shape to known active molecules. Despite its popularity, the retrieval of local molecular features that are critical to functional activity related to target binding often fails. To overcome this limitation, we developed a novel machine learning-based chemical binding similarity score by using various evolutionary relationships of binding targets. The chemical similarity was defined by the probability of chemical compounds binding to identical targets. Comprehensive and heterogeneous multiple target-binding chemical data were integrated into a paired data format and processed using multiple classification similarity-learning models with various levels of target evolutionary information. Encoding evolutionary information to chemical compounds through their binding targets substantially expanded available chemical-target interaction data and significantly improved model performance. The output probability of our integrated model, referred to as ensemble evolutionary chemical binding similarity (ensECBS), was effective for finding hidden chemical relationships. The developed method can serve as a novel chemical similarity tool that uses evolutionarily conserved target binding information.